基于微博签到数据的成渝城市群空间结构及其城际人口流动研究
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摘要
随着区域一体化进程的加快,中国城市群快速地发展起来,城市群城际间的人口流动研究得到了国内外学者的关注。城市群空间结构的研究以地理实体空间分析为主,城际人口流动的研究多使用传统统计数据,而将大数据运用于城市群空间结构及城际人口流动尚处于起步阶段。本研究基于新型的新浪微博用户签到的地理位置数据,研究成渝城市群的空间结构特征,并结合传统的社会经济统计数据对该区域人口流动的影响因素进行分析。研究发现:(1)微博签到数据进一步解释了成渝城市群呈现出"双核多中心"的组团特征,成都市和重庆主城构成了"双核";(2)微博人口流动的方向会受到行政区划的影响,微博人口流动的强度呈现出一定的等级差异;(3)微博人口流动的强度与方向同社会经济发展水平呈现出相对一致性,即地区生产总值越高、人口规模越大或交通联系强度越强,则人口流动越强烈。
With the rapid development of regional integration,nowadays the regional inter-city migration gets the more attention of the scholars at home and abroad.Micro-blog,as one of the most popular application in China,has become a hotspot of research in areas such as sociology and computer.Check-in,as one of Microblog's functions,can reflect the flow of inter-city population in real time.We used the crawler program to collect the research samples in the Chengdu-Chongqing urban agglomeration in January 2014.The information includes the Micro-blog's unique ID number,the grid coordinates of Micro-blog sending place,and the city code of the registered place,etc.By running this program,a total of 804204 valid Micro-blog check-in data weare obtained from the Chengdu-Chongqing urban agglomeration.Based on Micro-blog checking areas,this study analyzeds the spatial structure of the Chengdu-Chongqing urban agglomeration.And Wwe combined the micro-blog data with the traditional socioeconomic data,in order to analyze the impact factors of the regional migration.The results indicates that the spatial structure of micro-blog shows the characteristics of "many centers of dual-core" group in this area.There are only two cities whose micro-blog flows are more than 100,000.They are Chengdu and Chongqing,forming athe"dual-core".The direction of Micro-blog flow is affected by administrative division,and the intensity of Micro-blog flow presents a certain grade difference.The network shows an obvious hierarchy,and it closely correlatesnnects with the actual social-economic area closely,such as GDP,population size and the strength of traffic connection.For Chengdu and Chongqing,its GDP ranksed first and second,1,2 respectively,with athe population size all of greater than 7.59 million and both as a regional transport hubs,it makes their micro-blogWeibo flows areintensity in ranked 1 st and,2 nd,places respectively.Lastly,there are still some differences between Micro-blog's space and the actual geographic space inof Chengdu-Chongqing urban agglomeration.In the background of the national Yangtze River Economic Belt and China's new urbanization,we put the network information into the geographical space.Actually In this paper we discovered the spatial network characteristics of Chengdu-Chongqing urban agglomeration,and then this paper pointeds out the influence of socioeconomic factors on Micro-blog cyberspace flow.Of course,there may still be other factors behind Micro-blog's cyberspace,which need to be explored and analyzed in the future.
With the rapid development of regional integration,nowadays the regional inter-city migration gets the more attention of the scholars at home and abroad.Micro-blog,as one of the most popular application in China,has become a hotspot of research in areas such as sociology and computer.Check-in,as one of Microblog's functions,can reflect the flow of inter-city population in real time.We used the crawler program to collect the research samples in the Chengdu-Chongqing urban agglomeration in January 2014.The information includes the Micro-blog's unique ID number,the grid coordinates of Micro-blog sending place,and the city code of the registered place,etc.By running this program,a total of 804204 valid Micro-blog check-in data weare obtained from the Chengdu-Chongqing urban agglomeration.Based on Micro-blog checking areas,this study analyzeds the spatial structure of the Chengdu-Chongqing urban agglomeration.And Wwe combined the micro-blog data with the traditional socioeconomic data,in order to analyze the impact factors of the regional migration.The results indicates that the spatial structure of micro-blog shows the characteristics of "many centers of dual-core" group in this area.There are only two cities whose micro-blog flows are more than 100,000.They are Chengdu and Chongqing,forming athe"dual-core".The direction of Micro-blog flow is affected by administrative division,and the intensity of Micro-blog flow presents a certain grade difference.The network shows an obvious hierarchy,and it closely correlatesnnects with the actual social-economic area closely,such as GDP,population size and the strength of traffic connection.For Chengdu and Chongqing,its GDP ranksed first and second,1,2 respectively,with athe population size all of greater than 7.59 million and both as a regional transport hubs,it makes their micro-blogWeibo flows areintensity in ranked 1 st and,2 nd,places respectively.Lastly,there are still some differences between Micro-blog's space and the actual geographic space inof Chengdu-Chongqing urban agglomeration.In the background of the national Yangtze River Economic Belt and China's new urbanization,we put the network information into the geographical space.Actually In this paper we discovered the spatial network characteristics of Chengdu-Chongqing urban agglomeration,and then this paper pointeds out the influence of socioeconomic factors on Micro-blog cyberspace flow.Of course,there may still be other factors behind Micro-blog's cyberspace,which need to be explored and analyzed in the future.
引文
[1]Graham M.Neogeography and the palimpsests of place:Web2.0 and the construction of a virtual earth[J].Tidjschrift Voor Economishe en Social Geografie,2010,101(4):422-436.
[2]Haklay M,Singleton A,Parker C.Web mapping 2.0:The neogeography of the geoweb[J].Geography Compass,2008,2(6):2011-2039.
[3]Pinto N,António P A,Roca J.Applicability and calibration of an irregular cellular automata model for land use change[J].Computers,Environment and Urban Systems,2017,65:93-102.
[4]Arribasbel D,Sanz Gracia F.The validity of the monocentric city model in a polycentric age:US metropolitan areas in 1990,2000 and 2010[J].Urban Geography,2014,35(7):980-997.
[5]Melo P C,Graham D J,Graham.Transport-induced agglomeration effects:Evidence for US metropolitan areas[J].Regional Science Policy and Practice,2018,10(1):37-47.
[6]方创琳.城市群空间范围识别标准的研究进展与基本判断[J].城市规划学刊,2009,4(3):1-5.[Fang C L.Research progress and general definition about identification standards of urban agglomeration space[J].Urban Planning Forum,2009,4(3):1-5.]
[7]宋吉涛,方创琳,宋敦江.中国城市群空间结构的稳定性分析[J].地理学报,2006,61(12):1311-1325.[Song J T,Fang C L,Song D J.Spatial structure stability of urban agglomerations in China[J].Acta Geographica Sinica,2006,61(12):1311-1325.]
[8]姚士谋,陈爽.长江三角洲地区城市空间演化趋势[J].地理学报,1998,53(S):1-10.[Yao S M,Chen S.The trend of urban spatial evolution in the Yangtze river delta[J].Acta Geographica Sinica,1998,53(S):1-10.]
[9]王志宪,虞孝感,徐科峰.长江三角洲地区可持续发展的态势与对策[J].地理学报,2005,60(3):381-391.[Wang ZX,Yu X G,Xu K F.Situation and suggestions of sustainable development in the Yangtze river delta[J].Acta Geographica Sinica,2005,60(3):381-391.]
[10]蔺雪芹,方创琳.城市群工业发展的生态环境效应分析-以武汉城市群为例[J].地理研究,2010,29(12):2233-2243.[Lin X Q,Fang C L.Research on the eco-environment effect of industrial development in city group:Acase of Wuhan city group[J].Geographical Research,2010,29(12):2233-2243.]
[11]陈浩,陆林,郑嬗婷.基于旅游流的城市群旅游地旅游空间网络结构分析:以珠江三角洲城市群为例[J].地理学报,2011,66(2):257-266.[Chen H,Lu L,Zheng S T.Spatial network structure of the tourism destinations in urban agglomerations based on tourist flow:A case study of the Pearl river delta[J].Acta Geographica Sinica,2011,66(2)257-266.]
[12]方创琳.中国西部地区城市群形成发育现状与建设重点[J].干旱区地理,2010,33(5):667-675.[Fang C L.Development status and key points of construction of urban agglomerations in west regions of China[J].Arid Land Geography,2010,33(5):667-675.]
[13]何丰.成渝城市群空间结构与发展趋势研究[D].成都:四川省社会科学院,2016.[He F.Research on spatial structure and development trend of Chengdu-Chongqing urban agglomeration[D].Chengdu:Sichuan Academy of Social Sciences,2016.]
[14]程前昌.成渝城市群的生长发育与空间演化[D].上海:华东师范大学,2015.[Cheng Q C.Growth and spatial evolution of Chengdu-Chongqing urban agglomeration[D]Shanghai:East China Normal University,2015.]
[15]蒋奕廷,蒲波.基于引力模型的成渝城市群吸引力格局研究[J].软科学,2017,31(2):98-102.[Jiang Y T,Pu B.Analysis of attraction pattern in Chengdu-Chongqing urban agglomeration based on a gravity model[J].Soft Science2017,31(2):98-102.]
[16]王春杨,吴国誉,张超.基于DMSP/OLS夜间灯光数据的成渝城市群空间结构研究[J].城市发展研究,2015,22(11):20-24.[Wang C Y,Wu G Y,Zhang C.Research on spatial structure of the Chengdu-Chongqing urban agglomeration based on the DMSP/OLS night-time light data[J].Urban Development Studies,2015,22(11):20-24.]
[17]杨任飞,罗红霞,周盛,等.夜间灯光数据驱动的成渝城市群空间形成过程重建及分析[J].地球信息科学学报2017,19(5):653-661.[Yang R F,Luo H X,Zhou S,et al Restoring and analyzing the space forming process of Chengdu-Chongqing urban agglomeration by using DMSP/OLS night-time light data[J].Journal of Geo-information Science,2017,19(5):653-661.]
[18]方创琳.中国城市群研究取得的重要进展与未来发展方向[J].地理学报,2014,69(8):1130-1144.[Fang C L.Progress and the future direction of research into urban agglomeration in China[J].Acta Geographica Sinica,201469(8):1130-1144.]
[19]Lin J,Cromley R G.Evaluating geo-located Twitter data as a control layer for areal interpolation of population[J]Applied Geography,2015,58(2):41-47.
[20]Wong D W S,Huang Q Y.“Voting with their feet”:Delineating the sphere of influence using social media data[J].International Journal of Geo-information,2017,6(11):325-341.
[21]Graham M,Stephens M,Hale S.Featured graphic:Mapping the geoweb:A geography of Twitter[J].Environmen And Planning A,2013,45(1):100-102.
[22]王波,甄峰,席广亮,等.基于微博用户关系的网络信息地理研究--以新浪微博为例[J].地理研究,2013,32(2)380-391.[Wang B,Zhen F,Xi G L,et al.A study of cyber-geography based on Micro-blog users'relationship With a case of Micro-blog[J].Geographical Research2013,32(2):380-391.]
[23]王录仓,严翠霞,李巍.基于新浪微博大数据的旅游流时空特征研究--以兰州市为例[J].旅游学刊,2017,32(5):94-105.[Wang L C,Yan C X,Li W.Research on spatialtemporal characteristics of tourist flow based on Sina Micro-blog LBS data:A case study of Lanzhou[J].Tourism Tribune,2017,32(5):94-105.]
[24]宁鹏飞,万幼川,任福.面向新浪微博签到数据的时空热点事件检测方法[J].测绘与空间地理信息,2017,40(9):33-37.[Ning P F,Wan Y C,Ren F.Sina Micro-blog for the data of time and space hot event detection method[J].Geomatics and Spatial Information Technology,2017,40(9):33-37.]
[25]韩华瑞,代侦勇.湖北省微博签到活动空间差异分析--以新浪微博为例[J].测绘与空间地理信息,2016,39(10):159-162.[Han H R,Dai Z Y.The analysis of space difference of check-in activities in Hubei province:An empirical analysis of Sina Micro-blog[J].Geomatics and Spatial Information Technology,2016,39(10):159-162.]
[26]甄峰,王波,陈映雪.基于网络社会空间的中国城市网络特征-以新浪微博为例[J].地理学报,2012,67(8):1031-1043.[Zhen F,Wang B,Chen Y X.China's city network characteristics based on social network space:An empirical analysis of Sina Micro-blog[J].Acta Geographica Sinica,2012,67(8):1031-1043.]
[27]王贤文,王虹茵,李清纯.基于地理位置大数据的京津冀城市群短期人口流动研究[J].大连理工大学学报(社会科学版),2017,38(2):105-113.[Wang X W,Wang H Y,Li Q C.Location based big data analysis of the short-term population flow of Beijing,Tianjin and Hebei urban agglomeration[J].Journal of Dalian University of Technology,2017,38(2):105-113.]
[28]李长风.基于社交网络位置数据的区域流动空间特征研究--以长三角城市群为例[J].上海城市规划,2014(5):44-50.[Li C F.Study on the regional space of flows based on location data from social network:A case study of city group of Yangtze river delta[J].Shanghai Urban Planning Review,2014(5):44-50.]
[29]甄峰,王波.“大数据”热潮下人文地理学研究的再思考[J].地理研究,2015,34(5):803-811.[Zhen F,Wang B.Rethinking human geography in the age of big data[J].Geographical Research,2015,34(5):803-811.]
[30]武增海,李涛.高新技术开发区综合绩效空间分布研究--基于自然断点法的分析[J].统计与信息论坛,2013,28(3):82-88.[Wu Z H,Li T.The comprehensive performance evaluation of the high-tech development zone:Analysis based on the natural breakpoint method[J].Statistics and Information Forum,2013,28(3):82-88.]
[31]汪明峰,宁越敏.网络信息空间的城市地理学研究:综述与展望[J].地球科学进展,2002,17(6):854-863.[Wang M F,Ning Y M.The urban geography of cyberspace:Review and prospect[J].Advances in Earth Science,2002,17(6):854-863.]
[2]Haklay M,Singleton A,Parker C.Web mapping 2.0:The neogeography of the geoweb[J].Geography Compass,2008,2(6):2011-2039.
[3]Pinto N,António P A,Roca J.Applicability and calibration of an irregular cellular automata model for land use change[J].Computers,Environment and Urban Systems,2017,65:93-102.
[4]Arribasbel D,Sanz Gracia F.The validity of the monocentric city model in a polycentric age:US metropolitan areas in 1990,2000 and 2010[J].Urban Geography,2014,35(7):980-997.
[5]Melo P C,Graham D J,Graham.Transport-induced agglomeration effects:Evidence for US metropolitan areas[J].Regional Science Policy and Practice,2018,10(1):37-47.
[6]方创琳.城市群空间范围识别标准的研究进展与基本判断[J].城市规划学刊,2009,4(3):1-5.[Fang C L.Research progress and general definition about identification standards of urban agglomeration space[J].Urban Planning Forum,2009,4(3):1-5.]
[7]宋吉涛,方创琳,宋敦江.中国城市群空间结构的稳定性分析[J].地理学报,2006,61(12):1311-1325.[Song J T,Fang C L,Song D J.Spatial structure stability of urban agglomerations in China[J].Acta Geographica Sinica,2006,61(12):1311-1325.]
[8]姚士谋,陈爽.长江三角洲地区城市空间演化趋势[J].地理学报,1998,53(S):1-10.[Yao S M,Chen S.The trend of urban spatial evolution in the Yangtze river delta[J].Acta Geographica Sinica,1998,53(S):1-10.]
[9]王志宪,虞孝感,徐科峰.长江三角洲地区可持续发展的态势与对策[J].地理学报,2005,60(3):381-391.[Wang ZX,Yu X G,Xu K F.Situation and suggestions of sustainable development in the Yangtze river delta[J].Acta Geographica Sinica,2005,60(3):381-391.]
[10]蔺雪芹,方创琳.城市群工业发展的生态环境效应分析-以武汉城市群为例[J].地理研究,2010,29(12):2233-2243.[Lin X Q,Fang C L.Research on the eco-environment effect of industrial development in city group:Acase of Wuhan city group[J].Geographical Research,2010,29(12):2233-2243.]
[11]陈浩,陆林,郑嬗婷.基于旅游流的城市群旅游地旅游空间网络结构分析:以珠江三角洲城市群为例[J].地理学报,2011,66(2):257-266.[Chen H,Lu L,Zheng S T.Spatial network structure of the tourism destinations in urban agglomerations based on tourist flow:A case study of the Pearl river delta[J].Acta Geographica Sinica,2011,66(2)257-266.]
[12]方创琳.中国西部地区城市群形成发育现状与建设重点[J].干旱区地理,2010,33(5):667-675.[Fang C L.Development status and key points of construction of urban agglomerations in west regions of China[J].Arid Land Geography,2010,33(5):667-675.]
[13]何丰.成渝城市群空间结构与发展趋势研究[D].成都:四川省社会科学院,2016.[He F.Research on spatial structure and development trend of Chengdu-Chongqing urban agglomeration[D].Chengdu:Sichuan Academy of Social Sciences,2016.]
[14]程前昌.成渝城市群的生长发育与空间演化[D].上海:华东师范大学,2015.[Cheng Q C.Growth and spatial evolution of Chengdu-Chongqing urban agglomeration[D]Shanghai:East China Normal University,2015.]
[15]蒋奕廷,蒲波.基于引力模型的成渝城市群吸引力格局研究[J].软科学,2017,31(2):98-102.[Jiang Y T,Pu B.Analysis of attraction pattern in Chengdu-Chongqing urban agglomeration based on a gravity model[J].Soft Science2017,31(2):98-102.]
[16]王春杨,吴国誉,张超.基于DMSP/OLS夜间灯光数据的成渝城市群空间结构研究[J].城市发展研究,2015,22(11):20-24.[Wang C Y,Wu G Y,Zhang C.Research on spatial structure of the Chengdu-Chongqing urban agglomeration based on the DMSP/OLS night-time light data[J].Urban Development Studies,2015,22(11):20-24.]
[17]杨任飞,罗红霞,周盛,等.夜间灯光数据驱动的成渝城市群空间形成过程重建及分析[J].地球信息科学学报2017,19(5):653-661.[Yang R F,Luo H X,Zhou S,et al Restoring and analyzing the space forming process of Chengdu-Chongqing urban agglomeration by using DMSP/OLS night-time light data[J].Journal of Geo-information Science,2017,19(5):653-661.]
[18]方创琳.中国城市群研究取得的重要进展与未来发展方向[J].地理学报,2014,69(8):1130-1144.[Fang C L.Progress and the future direction of research into urban agglomeration in China[J].Acta Geographica Sinica,201469(8):1130-1144.]
[19]Lin J,Cromley R G.Evaluating geo-located Twitter data as a control layer for areal interpolation of population[J]Applied Geography,2015,58(2):41-47.
[20]Wong D W S,Huang Q Y.“Voting with their feet”:Delineating the sphere of influence using social media data[J].International Journal of Geo-information,2017,6(11):325-341.
[21]Graham M,Stephens M,Hale S.Featured graphic:Mapping the geoweb:A geography of Twitter[J].Environmen And Planning A,2013,45(1):100-102.
[22]王波,甄峰,席广亮,等.基于微博用户关系的网络信息地理研究--以新浪微博为例[J].地理研究,2013,32(2)380-391.[Wang B,Zhen F,Xi G L,et al.A study of cyber-geography based on Micro-blog users'relationship With a case of Micro-blog[J].Geographical Research2013,32(2):380-391.]
[23]王录仓,严翠霞,李巍.基于新浪微博大数据的旅游流时空特征研究--以兰州市为例[J].旅游学刊,2017,32(5):94-105.[Wang L C,Yan C X,Li W.Research on spatialtemporal characteristics of tourist flow based on Sina Micro-blog LBS data:A case study of Lanzhou[J].Tourism Tribune,2017,32(5):94-105.]
[24]宁鹏飞,万幼川,任福.面向新浪微博签到数据的时空热点事件检测方法[J].测绘与空间地理信息,2017,40(9):33-37.[Ning P F,Wan Y C,Ren F.Sina Micro-blog for the data of time and space hot event detection method[J].Geomatics and Spatial Information Technology,2017,40(9):33-37.]
[25]韩华瑞,代侦勇.湖北省微博签到活动空间差异分析--以新浪微博为例[J].测绘与空间地理信息,2016,39(10):159-162.[Han H R,Dai Z Y.The analysis of space difference of check-in activities in Hubei province:An empirical analysis of Sina Micro-blog[J].Geomatics and Spatial Information Technology,2016,39(10):159-162.]
[26]甄峰,王波,陈映雪.基于网络社会空间的中国城市网络特征-以新浪微博为例[J].地理学报,2012,67(8):1031-1043.[Zhen F,Wang B,Chen Y X.China's city network characteristics based on social network space:An empirical analysis of Sina Micro-blog[J].Acta Geographica Sinica,2012,67(8):1031-1043.]
[27]王贤文,王虹茵,李清纯.基于地理位置大数据的京津冀城市群短期人口流动研究[J].大连理工大学学报(社会科学版),2017,38(2):105-113.[Wang X W,Wang H Y,Li Q C.Location based big data analysis of the short-term population flow of Beijing,Tianjin and Hebei urban agglomeration[J].Journal of Dalian University of Technology,2017,38(2):105-113.]
[28]李长风.基于社交网络位置数据的区域流动空间特征研究--以长三角城市群为例[J].上海城市规划,2014(5):44-50.[Li C F.Study on the regional space of flows based on location data from social network:A case study of city group of Yangtze river delta[J].Shanghai Urban Planning Review,2014(5):44-50.]
[29]甄峰,王波.“大数据”热潮下人文地理学研究的再思考[J].地理研究,2015,34(5):803-811.[Zhen F,Wang B.Rethinking human geography in the age of big data[J].Geographical Research,2015,34(5):803-811.]
[30]武增海,李涛.高新技术开发区综合绩效空间分布研究--基于自然断点法的分析[J].统计与信息论坛,2013,28(3):82-88.[Wu Z H,Li T.The comprehensive performance evaluation of the high-tech development zone:Analysis based on the natural breakpoint method[J].Statistics and Information Forum,2013,28(3):82-88.]
[31]汪明峰,宁越敏.网络信息空间的城市地理学研究:综述与展望[J].地球科学进展,2002,17(6):854-863.[Wang M F,Ning Y M.The urban geography of cyberspace:Review and prospect[J].Advances in Earth Science,2002,17(6):854-863.]